Results 291 to 300 of about 546,017 (306)

Self-Supervised Transfer Learning with Shared Encoders for Cross-Domain Cloud Optimization

Yiqiang Zhou
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Contrastive self-supervised learning for neurodegenerative disorder classification. [PDF]

Front Neuroinform
Gryshchuk V, Singh D, Teipel S, Dyrba M, ADNI, AIBL, FTLDNI study groups.   +6 more
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A self-supervised learning approach for high throughput and high content cell segmentation. [PDF]

Commun Biol
Lam VK, Byers JM, Robitaille MC, Kaler L, Christodoulides JA, Raphael MP.   +5 more
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Self-supervised learning enhances accuracy and data efficiency in lower-limb joint moment estimation from gait kinematics. [PDF]

Front Bioeng Biotechnol
Li Y, He J, Liew B, Hollinger DS, Mei Q, Gholami B, Fasli M, McDonald-Maier K, Zhai X.   +8 more
europepmc   +1 more source

Self-supervised deep learning for advancing macromolecular analysis in cryo-electron tomography

Stojanovska, Frosina
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Enhanced gallbladder cancer detection via active and self-supervised learning integration: Innovating B-ultrasound image analysis. [PDF]

PLoS One
Li J, Zhou YQ.
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Self-Supervised Learning for Electroencephalography

IEEE Transactions on Neural Networks and Learning Systems
Decades of research have shown machine learning superiority in discovering highly nonlinear patterns embedded in electroencephalography (EEG) records compared with conventional statistical techniques. However, even the most advanced machine learning techniques require relatively large, labeled EEG repositories.
Mohammad H. Rafiei, Lynne V. Gauthier, Hojjat Adeli, Daniel Takabi   +3 more
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Self supervised Visual Geometry Learning

2021
Visual geometry learning aims to recover 3D geometry information i.e., surface normal, depth maps and camera poses from images. As a classic task in computer vision, this problem has been studied extensively for decades. It contains depth completion, stereo matching, monocular depth estimation, optical flow, visual odometry, structure from motion and ...
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Self-supervised learning

Abstract Supervised training requires pairs of target image and associated measurements, which are often difficult to collect. This chapter discusses self-supervised learning approaches based on constructing a self-supervised loss for training a neural network to map a measurement to a clean image.
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